--- title: "llm-lobbyist vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/johnnay-llm-lobbyist-vs-lm-sys-fastchat" tools: ["johnnay-llm-lobbyist", "lm-sys-fastchat"] --- # llm-lobbyist vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-lobbyist when llm-lobbyist is primarily Jupyter Notebook; FastChat is Python; pick FastChat when fastChat is primarily Python; llm-lobbyist is Jupyter Notebook. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) reports 174 GitHub stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Code for the paper: "Large Language Models as Corporate Lobbyists" (2023). | An open platform for training, serving, and evaluating large language models | | Stars | 174 | 39,490 | | Forks | 14 | 4,788 | | Open issues | 0 | 1,027 | | Language | Jupyter Notebook | Python | | Adopt for | - | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks, Vector Databases | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1275d | 71d | | Open issues (now) | 0 | 1.0k | | Owner type | User | Organization | | Full report | [trust report](/tools/johnnay-llm-lobbyist/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Decision facts: FastChat - **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB ## Choose when ### Choose llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; FastChat is Python. - Tags unique to llm-lobbyist: jupyter notebook. - Also covers Vector Databases. ### Choose FastChat if… - FastChat is primarily Python; llm-lobbyist is Jupyter Notebook. - Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models. - Also covers Inference & Serving, Model Training. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use llm-lobbyist - Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use FastChat - - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on - + Mac. ## Common questions ### What is the difference between llm-lobbyist and FastChat? llm-lobbyist: Code for the paper: "Large Language Models as Corporate Lobbyists" (2023).. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-lobbyist over FastChat? Choose llm-lobbyist over FastChat when llm-lobbyist is primarily Jupyter Notebook; FastChat is Python; Tags unique to llm-lobbyist: jupyter notebook; Also covers Vector Databases. ### When should I choose FastChat over llm-lobbyist? Choose FastChat over llm-lobbyist when FastChat is primarily Python; llm-lobbyist is Jupyter Notebook; Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models; Also covers Inference & Serving, Model Training; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid llm-lobbyist? Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid FastChat? - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac. ### Is llm-lobbyist or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 174). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-lobbyist and FastChat open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-lobbyist or FastChat? GraphCanon lists graph-backed alternatives at [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/johnnay-llm-lobbyist-vs-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-lobbyist or FastChat? llm-lobbyist: Dormant. FastChat: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for llm-lobbyist and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=johnnay-llm-lobbyist`](/api/graphcanon/graph?tool=johnnay-llm-lobbyist) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_